SPI from CHRPS
AnnaClaire Marley
8/1/2019
- This Rmarkdown changes CHRPS daily precipitation (mm) to SPI
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## sum_mts_swe_m3 = col_double()
## )Whole Navajo Nation
Precipitation
6 month SPI
##
## Call:
## lm(formula = nn_spi$spi ~ nn_spi$date)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1086 -0.5897 0.0332 0.6268 2.5886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6392680 0.1344391 4.755 2.67e-06 ***
## nn_spi$date -0.0000576 0.0000114 -5.054 6.29e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9752 on 454 degrees of freedom
## Multiple R-squared: 0.05326, Adjusted R-squared: 0.05118
## F-statistic: 25.54 on 1 and 454 DF, p-value: 6.287e-07NN SPI vs PDSI
## Parsed with column specification:
## cols(
## date = col_date(format = ""),
## pdsi = col_double()
## )
- SPI and PDSI have a pretty high correlation
All mountains SWE vs NN SPI
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]- April SPI and high elevation swe anomaly have strongest correlation
without 2010
- taking out 2010 actually just makes it worse
- May positive swe & positive spi / positive swe: 50%
- May negative swe & negative spi / negative swe: 66.6666667%
- May total correct: 60%
- April positive swe & positive spi / positive swe: 57.143%
- April negative swe & negative spi / negative swe: 77.7777778%
- April total correct: 68.75%
- every time in april there was a drought warning or a drought emergency, it was preceded by a negative anomaly winter swe
- drought emergency occured in 2018, with drought warnings in 2006 and 2012
## waterYear spi swe_anom drought
## 1 2006 -1.243734 -0.8939313 warning
## 2 2012 -1.108299 -0.2017344 warning
## 3 2018 -1.775837 -0.9271395 emergency
Watersheds
April SPI and Chuska Watershed Anomaly Correlations
- Middle San Juan April SPI has the highest correlation with Chuska winter swe anom, chinle has the lowest
- Chinle also has the lowest correlations with other watersheds’ SPIs
May SPI and Chuska Watershed Anomaly Correlations
- the correlations for may spi across all regions is much lower than for april
- Middle San Juan still had the higest correlation to Chuska, and Chinle has the least
Middle San Juan
SPI
##
## Call:
## lm(formula = mid_sj_spi$spi ~ mid_sj_spi$date)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.10335 -0.58415 0.02294 0.67057 2.42082
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.015e-01 1.359e-01 3.690 0.000251 ***
## mid_sj_spi$date -4.518e-05 1.152e-05 -3.921 0.000102 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9857 on 454 degrees of freedom
## Multiple R-squared: 0.03276, Adjusted R-squared: 0.03063
## F-statistic: 15.38 on 1 and 454 DF, p-value: 0.0001017Chuska Monthly Winter SWE Anomaly vs SPI
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]- April SPI has the highest correlation (almost significantly!)
April SPI and Chuska swe
- April positive swe & positive spi / positive swe: 83.333%
- April negative swe & negative spi / negative swe: 70%
- April total correct: 75%
- drought emergency in 2012 and 2018
## waterYear watershed spi swe_anom drought
## 1 2006 middle_sj -1.479390 -0.9098490 warning
## 2 2012 middle_sj -1.574148 -0.1368591 emergency
## 3 2018 middle_sj -2.028572 -0.9364887 emergency
Chaco
SPI
##
## Call:
## lm(formula = chaco_spi$spi ~ chaco_spi$date)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5627 -0.6231 0.0548 0.6595 2.3534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.260e-01 1.346e-01 4.651 4.35e-06 ***
## chaco_spi$date -5.639e-05 1.141e-05 -4.942 1.09e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9764 on 454 degrees of freedom
## Multiple R-squared: 0.05105, Adjusted R-squared: 0.04896
## F-statistic: 24.42 on 1 and 454 DF, p-value: 1.089e-06Chuska Monthly Winter SWE Anomaly vs SPI
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]- April SPI has the highest correlation (almost significantly!)
April SPI and Chuska swe
- April positive swe & positive spi / positive swe: 50%
- April negative swe & negative spi / negative swe: 70%
- April total correct: 62.5%
- drought emergencies in 2006 and 2018
## waterYear watershed spi swe_anom drought
## 1 2006 chaco -1.663954 -0.90984896 emergency
## 2 2011 chaco -1.052243 -0.08834293 warning
## 3 2012 chaco -1.321458 -0.13685911 warning
## 4 2014 chaco -1.105010 -0.67909787 warning
## 5 2018 chaco -2.226117 -0.93648871 emergencyUpper Puerco
Chuska Monthly Winter SWE Anomaly vs SPI
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]April SPI and Chuska swe
- April positive swe & positive spi / positive swe: 66.667%
- April negative swe & negative spi / negative swe: 80%
- April total correct: 75%
Drought severity in spring
- there was a drought emergency in 2006 & 2018
## waterYear watershed spi swe_anom drought
## 1 2006 upper_puerco -1.627618 -0.9098490 emergency
## 2 2012 upper_puerco -1.464829 -0.1368591 warning
## 3 2013 upper_puerco -1.008209 0.3904031 warning
## 4 2018 upper_puerco -1.545492 -0.9364887 emergencyChinle
SPI
##
## Call:
## lm(formula = chinle_spi$spi ~ chinle_spi$date)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55771 -0.44516 -0.02485 0.47069 1.39207
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.175e+00 8.549e-02 25.45 <2e-16 ***
## chinle_spi$date -1.961e-04 7.248e-06 -27.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6202 on 454 degrees of freedom
## Multiple R-squared: 0.6171, Adjusted R-squared: 0.6163
## F-statistic: 731.8 on 1 and 454 DF, p-value: < 2.2e-16Chuska Monthly Winter SWE Anomaly vs SPI
## TableGrob (2 x 2) "arrange": 4 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
## 3 3 (2-2,1-1) arrange gtable[layout]
## 4 4 (2-2,2-2) arrange gtable[layout]April SPI and Chuska swe
- April positive swe & positive spi / positive swe: 0%
- April negative swe & negative spi / negative swe: 80%
- April total correct: 50%
Drought severity in spring
plot_spi_drought_level(chinle_spi,
month = c(4),
region_time = "Navajo Nation March, April, May 6 month") 
plot_spi_drought_level(chinle_spi,
month = all_months,
region_time = "Navajo Nation March, April, May 6 month",
years = "all")
- drought emergency occured in 2006, 2012, 2018
## waterYear watershed spi swe_anom drought
## 1 2006 chinle -1.600052 -0.90984896 emergency
## 2 2007 chinle -1.314274 -0.85620097 warning
## 3 2009 chinle -1.094717 -0.14640716 warning
## 4 2011 chinle -1.232336 -0.08834293 warning
## 5 2012 chinle -1.600052 -0.13685911 emergency
## 6 2013 chinle -1.172496 0.39040309 warning
## 7 2014 chinle -1.377450 -0.67909787 warning
## 8 2016 chinle -1.019086 0.04659200 warning
## 9 2018 chinle -1.923309 -0.93648871 emergency## watershed april_negative april_positive april_total
## 1 Middle San Juan 0.7 0.8333333 0.750
## 2 Chaco 0.7 0.5000000 0.625
## 3 Upper Puerco 0.8 0.6666667 0.750
## 4 Chinle 0.8 0.0000000 0.500Relationships between CHuska Swe ANom and April watershed SPI distributions
